Things aren’t so bad … so let’s not make them worse

By Graham

The People think they want change

Ok, there is a lot of anger out there. Some are angry because they fear the ‘other’ taking away what they have; others are angry because they want more redistribution and fairness. Some blame benefit-scrounging immigrants, others blame the global elites. But while the grumbles might be diverse, there is a common sense that the system is somehow ‘broken’ in a way it wasn’t before. Whether it means Trump, Brexit or someone like Bernie Sanders, a large number of people who previously would have been moderates now want – or at very least expect – to see fundamental change to the societies and political systems they consider have failed. Alarmingly, they seem prepared to topple long-established systems and political traditions in order to see this change happen.

And maybe they’re right. Who knows? The consequences of disruptive change are hard to predict in the short run, and ultimately may take a very long time to play out fully. When Nixon visited Beijing in 1972, he asked the witty and influential Chinese mandarin Zhou Enlai his opinion on what impact the 1789 French Revolution had had on history, to which Enlai is said to have replied that it was “too early to say”.

Nature red in tooth and claw

But it seems to me the risks are very much on the downside. To paraphrase John Lennon, if disruptive change means destruction, you can count me out . Our system may not be perfect, but it is a hell of a lot better than nothing. To see how, consider what things would be like in the complete absence of society. Imagine an invisible hand picked any one of us up from his current desk, couch, bed, airplane seat … and carried him out of his man-made environment and place him, naked, in a

Without us around, Nature would reclaim cities within a few hundred years. Romantic? Or just plain brutal?

Without civilisation, Nature would reclaim cities within a few hundred years. Romantic? Or brutal and hostile?

theoretical primeval forest where other humans simply did not exist. This is the total absence of society. How well would he fare? He might last a week before getting eaten. A summer, perhaps, if he is particularly crafty and in good health. But come winter, he would freeze, starve or get eaten by wolves. The first major injury or illness would likely finish him off. And even if, by some miracle, he managed to carve out a niche (most likely literally) for himself, would his quality of life be even a fraction of what it is now? I remember an excellent article written by Alan Weisman in Discover Magazine back in 2005 which explored the world without humans. It was a romantic vision, full of evocative prose of species flourishing and cities crumbling. The descriptions made it clear a little bit of the author’s heart longed for such a thing to take place. Yet where is Mr Weisman now? Living in one of the few remaining wildernesses in Alaska or Russia which closely approximate his vision? I’m guessing not. Especially as he was sending pre-apocalyptic tweets as recently as 2014.

Creature comforts are better than creatures

This thought exercise is designed to remind us of just what a good job society does at shielding us from what is, in reality, a hostile physical environment. Such a good job, in fact, that we forget we are being shielded. Unlike the current political system, Nature isn’t just guilty of neglecting our interests and selling us a bit of Fake News. Nature actively wants us to die. It wants to dispatch predators to eat us, it wants to release diseases to sicken us, or else simply deny us food and watch us starve. The system, far from being broken, does an absolutely remarkable job of taming Nature and providing us with far more than what we could have on our own. What’s more, it is better at doing this now, than at any point in human history.

If we allow this system to be torn down, perhaps a better one will rise from the ashes and we will achieve some kind of Utopia. But that seems like a bad bet, given what we know from history and observing the physical world around us. Disruptive change is more likely to give Nature the opening she has been seeking for centuries. She might rub her hands in glee while we starve in our billions. Animals or bacteria could so easily overwhelm us, and in the ensuring mayhem we would likely turn on each other. Then, somewhere in the mud and mess, those among the living who were old enough to remember would regret that they so cheaply threw away a system they thought was broken, but in reality was only a little bit flawed.


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As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:


While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.


We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values: